We further investigate the domain shift between the WORD dataset and two widely-used public datasets BTCV [1]} and TCIA [2]}. The differences between the WORD and BTCV [1]}, TCIA [2]} lie in : 1) coming from different centers/scaners/countries; 2) suffering from different diseases; 3) with different phase/contrast enhancement; 4) with different voxel spacing; 5) with different annotators. All of them could affect the generalizability of the deep network and further limit the clinical practice. In this work, we use the pre-trained model on the WORD dataset to inference the samples from BTCV [1]} (47 scans) and TCIA [2]} (43 scans) to estimate the domain gap. Tab. REF  and Tab. REF  list the results of \(DSC\)  and \(HD_{95}\) . Here, we just consider the official annotated organs of the BTCV [1]} and TCIA [2]} datasets. It can be found that there are very significant domain shifts between WORD and BTCV [1]}, TCIA [2]} datasets, as the pre-trained nnUNet on the WORD dataset performs very worse on the BTCV [1]} and TCIA [2]}. It also indicates that the model generalization for the multi-site abdominal organs task is not a solved problem.
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